(3R,4S,5R,6R)-3,4,5-tris(benzyloxy)-6-methyltetrahydro-2H-pyran-2-one (1) is a key intermediate for the preparation of promising SGLT2 inhibitors currently undergoing clinical tests for diabetes therapy. However, fewer reports have demonstrated the preparation of compound 1 at an industrial scale. In this article, an efficient preparation of the intermediate for the industrial production was explored from commercially available methyl-α-D-glucopyranoside in seven steps, including TBS protection, benzyl protection, TBS removal, iodination, reduction, demethylation, and oxidation. The batch of the validation process was 42.82 kg with a HPLC purity of 99.31%. The main advantages of this approach are that the total cost is lower than the reported laboratory-scale synthetic method, the quality is reproducible, and the process is safe and environmentally friendly.
Background: To parse the characteristics of aneuploidy related riskscore (ARS) model in head and neck squamous cell carcinomas (HNSC) and their predictive ability on patient prognosis. Methods: Molecular subtyping of HNSC specimens was clustered by Copy Number Variation (CNV) data from The Cancer Genome Atlas (TCGA) dataset applying consistent clustering, followed by immune condition evaluation, differentially expressed genes (DEGs) analysis and DEGs function annotation. Weighted gene co-expression network analysis (WGCNA), protein-protein interaction, Univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox regression analysis were implemented to construct an ARS model. A nomogram for clinic practice was designed by rms package. Immunotherapy evaluation and drug sensitivity prediction were also carried out. Results: We stratified HNSC patients into three different molecular subgroups, with the best prognosis in C1 cluster among 3 clusters. C1 cluster displayed greatest immune infiltration status. The most DEGs between C1 and C2 groups, mainly enriched in cell cycle and immune function. We constructed a nine-gene ARS model (ICOS, IL21R, CCR7, SELL, CYTIP, ZAP70, CCR4, S1PR4 and CD79A) that effectively differentiates between high- and low-risk patients. Patients in low ARS group showed a higher sensitivity to immunotherapy. A nomogram built by integrating ARS and clinic-pathological characteristics helped predict clinic survival benefit. Drug sensitivity evaluation found that 4/9 inhibitor drugs (MK-8776, AZD5438, PD-0332991, PHA-665752) acted on the cell cycle. Conclusions: We classified 3 molecular subtypes for HNSC patients and established an ARS prognostic model, which offered a prospective direction for prognosis in HNSC.
Background: Thyroid cancer represents the most prevalent malignant endocrine tumour, with rising incidence worldwide and high mortality rates among patients exhibiting dedifferentiation and metastasis. Effective biomarkers and therapeutic interventions are warranted in aggressive thyroid malignancies. The transcription factor 19 (TCF19) gene has been implicated in conferring a malignant phenotype in cancers. However, its contribution to thyroid neoplasms remains unclear. Results: In this study, we performed genome-wide and phenome-wide association studies to identify a potential causal relationship between TCF19 and thyroid cancer. Our analyses revealed significant associations between TCF19 and various autoimmune diseases and human cancers, including cervical cancer and autoimmune thyroiditis, with a particularly robust signal for the deleterious missense variation rs2073724 that is associated with thyroid function, hypothyroidism, and autoimmunity. Furthermore, functional assays and transcriptional profiling in thyroid cancer cells demonstrated that TCF19 regulates important biological processes, especially inflammatory and immune responses. We demonstrated that TCF19 could promote the progression of thyroid cancer in vitro and in vivo and the C>T variant of rs2073724 disrupted TCF19 protein binding to target gene promoters and their expression, thus reversing the effect of TCF19 protein. Conclusions: Taken together, these findings implicate TCF19 as a promising therapeutic target in aggressive thyroid malignancies and designate rs2073724 as a causal biomarker warranting further investigation in thyroid cancer.
Abstract This study assessed the effect of GLP-1 based therapies on atherosclerotic markers in type 2 diabetes patients. 31 studies were selected to obtain data after multiple database searches and following inclusion and exclusion criteria. Age and BMI of the participants of longitudinal studies were 59.8 ± 8.3 years and 29.2 ± 5.7 kg/m 2 (Mean±SD). Average duration of GLP-1 based therapies was 20.5 weeks. Percent flow-mediated diameter (%FMD) did not change from baseline significantly but when compared to controls, %FMD increased non-significantly following GLP-1-based therapies (1.65 [−0.89, 4.18]; P = 0.2; REM) in longitudinal studies and increased significantly in cross sectional studies (2.58 [1.68, 3.53]; P < 0.00001). Intima media thickness decreased statistically non-significantly by the GLP-1 based therapies. GLP-1 based therapies led to statistically significant reductions in the serum levels of brain natriuretic peptide (−40.16 [−51.50, −28.81]; P < 0.0001; REM), high sensitivity c-reactive protein (−0.27 [−0.48, −0.07]; P = 0.009), plasminogen activator inhibitor-1 (−12.90 [−25.98, 0.18]; P=0.05), total cholesterol (−5.47 [−9.55, −1.39]; P = 0.009), LDL-cholesterol (−3.70 [−7.39, −0.00]; P = 0.05) and triglycerides (−16.44 [−25.64, −7.23]; P = 0.0005) when mean differences with 95% CI in the changes from baselines were meta-analyzed. In conclusion, GLP-1-based therapies appear to provide beneficial effects against atherosclerosis. More randomized data will be required to arrive at conclusive evidence.
Non-small cell lung cancer (NSCLC) accounts for the majority of lung cancer but with a low early diagnosis rate. With immunotherapy becomes popular in lung cancer, single immunotherapy drug treatment as the first-line or second-line plays an important role. The meta-analysis compares different clinical effects of them by overall survival (OS) and progression-free survival (PFS) because it is important to detect the best time of immunotherapy for NSCLC patients.Randomized controlled trials (RCTs) were selected by using the Cochrane Library, Embase, PubMed and Web of science. Pool the hazard ratio (HR) and use the PFS, OS as outcomes.Ten RCTs were included. The pooled results indicated that first-line and second-line single immunotherapy drug treatment seems to have a tiny difference in PFS, with HR 0.79, 95% confidence interval (CI): 0.51-1.21, I2 =89% in first-line single immunotherapy drug treatment and HR 0.74, 95% CI: 0.62-0.89, I2 =84% in second-line single immunotherapy drug treatment. When it comes to OS, first-line immunotherapy drug treatment still has better effects than the second-line. In first-line single immunotherapy drug treatment, HR 0.78, 95% CI: 0.55-1.11, I2 =83%, but in second-line, HR 0.70, 95% CI: 0.64-0.76, I2 =53%.First-line single drug immunotherapy had the tendency better than single immunotherapy drugs used in second-line treatment.
In recent years, plasma medicine has attracted more and more attentions with the fast development of atmospheric plasmas technology. The experimental measurements have definitely shown some positive effects on the biological tissue and materials, however, the mechanisms of interactions between atmospheric plasma and cells are still unknown. In this paper, we coupled the fluid model and multi-layer dielectric of spherical cell to investigate the temporal-spatial distribution of electric fields in the cells induced by the atmospheric plasmas generated by dielectric barrier discharges. From the simulation data, the voltage on the cell almost totally drops on plasma membrane and the voltage on the plasma membrane is much larger than that of normal cells. On the other hand, both the transmembrane potentials of inner and outer membrane rise very fast during the process of ignition, then the former drops quickly after the breakdown, but the later sustains at a relatively large value until the next breakdown event. The influences on the voltage distribution in cell of applied voltage and excitation frequency are also discussed based on the simulation data. At present, the numerical investigations on the interaction of plasma and cells still need to be greatly developed, and the given simulation in this work will show some insights to the further theoretical study.
Abstract This review provides an essential overview on the progress of rapidly-developing China’s radiopharmaceuticals in recent years (2014–2021). Our discussion reflects on efforts to develop potential, preclinical, and in-clinical radiopharmaceuticals including the following areas: (1) brain imaging agents, (2) cardiovascular imaging agents, (3) infection and inflammation imaging agents, (4) tumor radiopharmaceuticals, and (5) boron delivery agents (a class of radiopharmaceutical prodrug) for neutron capture therapy. Especially, the progress in basic research, including new radiolabeling methodology, is highlighted from a standpoint of radiopharmaceutical chemistry. Meanwhile, we briefly reflect on the recent major events related to radiopharmaceuticals along with the distribution of major R&D forces (universities, institutions, facilities, and companies), clinical study status, and national regulatory supports. We conclude with a brief commentary on remaining limitations and emerging opportunities for China’s radiopharmaceuticals.
Background Deep learning (DL) has been used on medical images to grade, differentiate, and predict prognosis in many tumors. Purpose To explore the effect of computed tomography (CT)-based deep learning nomogram (DLN) for predicting cervical cancer lymph node metastasis (LNM) before surgery. Material and Methods In total, 418 patients with stage IB-IIB cervical cancer were retrospectively enrolled for model exploration (n = 296) and internal validation (n = 122); 62 patients from another independent institution were enrolled for external validation. A convolutional neural network (CNN) was used for DL features extracting from all lesions. The least absolute shrinkage and selection operator (Lasso) logistic regression was used to develop a deep learning signature (DLS). A DLN incorporating the DLS and clinical risk factors was proposed to predict LNM individually. The performance of the DLN was evaluated on internal and external validation cohorts. Results Stage, CT-reported pelvic lymph node status, and DLS were found to be independent predictors and could be used to construct the DLN. The combination showed a better performance than the clinical model and DLS. The proposed DLN had an area under the curve (AUC) of 0.925 in the training cohort, 0.771 in the internal validation cohort, and 0.790 in the external validation cohort. Decision curve analysis and stratification analysis suggested that the DLN has potential ability to generate a personalized probability of LNM in cervical cancer. Conclusion The proposed CT-based DLN could be used as a personalized non-invasive tool for preoperative prediction of LNM in cervical cancer, which could facilitate the choice of clinical treatment methods.
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